Merge 3.4

modules/core/include/opencv2/core/bindings_utils.hpp

@@ -8,6 +8,8 @@
 #include <opencv2/core/async.hpp>
 #include <opencv2/core/detail/async_promise.hpp>
 
+#include <stdexcept>
+
 namespace cv { namespace utils {
 //! @addtogroup core_utils
 //! @{
@@ -113,6 +115,12 @@ String dumpRange(const Range& argument)
     }
 }
 
+CV_WRAP static inline
+void testRaiseGeneralException()
+{
+    throw std::runtime_error("exception text");
+}
+
 CV_WRAP static inline
 AsyncArray testAsyncArray(InputArray argument)
 {

modules/dnn/include/opencv2/dnn/shape_utils.hpp

@@ -205,21 +205,33 @@ static inline std::ostream& operator<<(std::ostream &out, const MatShape& shape)
     return out;
 }
 
-inline int clamp(int ax, int dims)
+/// @brief Converts axis from `[-dims; dims)` (similar to Python's slice notation) to `[0; dims)` range.
+static inline
+int normalize_axis(int axis, int dims)
 {
-    return ax < 0 ? ax + dims : ax;
+    CV_Check(axis, axis >= -dims && axis < dims, "");
+    axis = (axis < 0) ? (dims + axis) : axis;
+    CV_DbgCheck(axis, axis >= 0 && axis < dims, "");
+    return axis;
 }
 
-inline int clamp(int ax, const MatShape& shape)
+static inline
+int normalize_axis(int axis, const MatShape& shape)
 {
-    return clamp(ax, (int)shape.size());
+    return normalize_axis(axis, (int)shape.size());
 }
 
-inline Range clamp(const Range& r, int axisSize)
+static inline
+Range normalize_axis_range(const Range& r, int axisSize)
 {
-    Range clamped(std::max(r.start, 0),
+    if (r == Range::all())
+        return Range(0, axisSize);
+    CV_CheckGE(r.start, 0, "");
+    Range clamped(r.start,
                   r.end > 0 ? std::min(r.end, axisSize) : axisSize + r.end + 1);
-    CV_Assert_N(clamped.start < clamped.end, clamped.end <= axisSize);
+    CV_DbgCheckGE(clamped.start, 0, "");
+    CV_CheckLT(clamped.start, clamped.end, "");
+    CV_CheckLE(clamped.end, axisSize, "");
     return clamped;
 }
 

modules/dnn/include/opencv2/dnn/version.hpp

@@ -6,7 +6,7 @@
 #define OPENCV_DNN_VERSION_HPP
 
 /// Use with major OpenCV version only.
-#define OPENCV_DNN_API_VERSION 20201117
+#define OPENCV_DNN_API_VERSION 20210205
 
 #if !defined CV_DOXYGEN && !defined CV_STATIC_ANALYSIS && !defined CV_DNN_DONT_ADD_INLINE_NS
 #define CV__DNN_INLINE_NS __CV_CAT(dnn4_v, OPENCV_DNN_API_VERSION)

modules/dnn/src/dnn.cpp

@@ -2972,7 +2972,7 @@ struct Net::Impl : public detail::NetImplBase
                 // the concatenation optimization is applied with batch_size > 1.
                 // so, for now, we only apply this optimization in the most popular
                 // case batch_size == 1.
-                int axis = clamp(concatLayer->axis, output.dims);
+                int axis = normalize_axis(concatLayer->axis, output.dims);
                 if( output.total(0, axis) == 1 )
                 {
                     size_t i, ninputs = ld.inputBlobsId.size();

modules/dnn/src/layers/concat_layer.cpp

@@ -79,7 +79,7 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer
     {
         CV_Assert(inputs.size() > 0);
         outputs.resize(1, inputs[0]);
-        int cAxis = clamp(axis, inputs[0]);
+        int cAxis = normalize_axis(axis, inputs[0]);
 
         int axisSum = 0;
         for (size_t i = 0; i < inputs.size(); i++)
@@ -201,7 +201,7 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer
         inps.getUMatVector(inputs);
         outs.getUMatVector(outputs);
 
-        int cAxis = clamp(axis, inputs[0].dims);
+        int cAxis = normalize_axis(axis, inputs[0].dims);
         if (padding)
             return false;
 
@@ -255,7 +255,7 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer
         inputs_arr.getMatVector(inputs);
         outputs_arr.getMatVector(outputs);
 
-        int cAxis = clamp(axis, inputs[0].dims);
+        int cAxis = normalize_axis(axis, inputs[0].dims);
         Mat& outMat = outputs[0];
 
         if (padding)
@@ -296,7 +296,7 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer
         auto context = reinterpret_cast<csl::CSLContext*>(context_);
 
         auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
-        auto concat_axis = clamp(axis, input_wrapper->getRank());
+        auto concat_axis = normalize_axis(axis, input_wrapper->getRank());
         return make_cuda_node<cuda4dnn::ConcatOp>(preferableTarget, std::move(context->stream), concat_axis, padding);
     }
 #endif
@@ -305,7 +305,7 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer
     {
 #ifdef HAVE_VULKAN
         vkcom::Tensor in = VkComTensor(input[0]);
-        int cAxis = clamp(axis, in.dimNum());
+        int cAxis = normalize_axis(axis, in.dimNum());
         std::shared_ptr<vkcom::OpBase> op(new vkcom::OpConcat(cAxis));
         return Ptr<BackendNode>(new VkComBackendNode(input, op));
 #endif // HAVE_VULKAN
@@ -341,7 +341,7 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
 
         InferenceEngine::Builder::ConcatLayer ieLayer(name);
-        ieLayer.setAxis(clamp(axis, input->getDims().size()));
+        ieLayer.setAxis(normalize_axis(axis, input->getDims().size()));
         ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
@@ -354,7 +354,7 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer
     {
         InferenceEngine::DataPtr data = ngraphDataNode(inputs[0]);
         const int numDims = data->getDims().size();
-        const int cAxis = clamp(axis, numDims);
+        const int cAxis = normalize_axis(axis, numDims);
         std::vector<size_t> maxDims(numDims, 0);
 
         CV_Assert(inputs.size() == nodes.size());

modules/dnn/src/layers/flatten_layer.cpp

@@ -89,8 +89,8 @@ class FlattenLayerImpl CV_FINAL : public FlattenLayer
         }
 
         int numAxes = inputs[0].size();
-        int startAxis = clamp(_startAxis, numAxes);
-        int endAxis = clamp(_endAxis, numAxes);
+        int startAxis = normalize_axis(_startAxis, numAxes);
+        int endAxis = normalize_axis(_endAxis, numAxes);
 
         CV_Assert(startAxis >= 0);
         CV_Assert(endAxis >= startAxis && endAxis < (int)numAxes);
@@ -120,8 +120,8 @@ class FlattenLayerImpl CV_FINAL : public FlattenLayer
         inputs_arr.getMatVector(inputs);
 
         int numAxes = inputs[0].dims;
-        _startAxis = clamp(_startAxis, numAxes);
-        _endAxis = clamp(_endAxis, numAxes);
+        _startAxis = normalize_axis(_startAxis, numAxes);
+        _endAxis = normalize_axis(_endAxis, numAxes);
     }
 
 #ifdef HAVE_OPENCL
@@ -195,8 +195,8 @@ virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inp
         std::vector<size_t> dims = ieInpNode->get_shape();
 
         int numAxes = dims.size();
-        int startAxis = clamp(_startAxis, numAxes);
-        int endAxis = clamp(_endAxis, numAxes);
+        int startAxis = normalize_axis(_startAxis, numAxes);
+        int endAxis = normalize_axis(_endAxis, numAxes);
 
         CV_Assert(startAxis >= 0);
         CV_Assert(endAxis >= startAxis && endAxis < numAxes);

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -132,7 +132,7 @@ class FullyConnectedLayerImpl CV_FINAL : public InnerProductLayer
             CV_CheckEQ(blobs[0].dims, 2, "");
             numOutput = blobs[0].size[0];
             CV_Assert(!bias || (size_t)numOutput == blobs[1].total());
-            cAxis = clamp(axis, inputs[0]);
+            cAxis = normalize_axis(axis, inputs[0]);
         }
 
         MatShape outShape(cAxis + 1);
@@ -356,7 +356,7 @@ class FullyConnectedLayerImpl CV_FINAL : public InnerProductLayer
             return true;
         }
 
-        int axisCan = clamp(axis, inputs[0].dims);
+        int axisCan = normalize_axis(axis, inputs[0].dims);
         int numOutput = blobs[0].size[0];
         int innerSize = blobs[0].size[1];
         int outerSize = total(shape(inputs[0]), 0, axisCan);
@@ -477,7 +477,7 @@ class FullyConnectedLayerImpl CV_FINAL : public InnerProductLayer
 
         if (!blobs.empty())
         {
-            int axisCan = clamp(axis, input[0].dims);
+            int axisCan = normalize_axis(axis, input[0].dims);
             int outerSize = input[0].total(0, axisCan);
 
             for (size_t i = 0; i < input.size(); i++)
@@ -525,7 +525,7 @@ class FullyConnectedLayerImpl CV_FINAL : public InnerProductLayer
 
         auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
 
-        auto flatten_start_axis = clamp(axis, input_wrapper->getRank());
+        auto flatten_start_axis = normalize_axis(axis, input_wrapper->getRank());
 
         auto biasMat_ = bias ? biasMat : Mat();
         return make_cuda_node<cuda4dnn::InnerProductOp>(preferableTarget, std::move(context->stream), std::move(context->cublas_handle), flatten_start_axis, weightsMat, biasMat_);

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -126,8 +126,8 @@ class NormalizeBBoxLayerImpl CV_FINAL : public NormalizeBBoxLayer
 
         const UMat& inp0 = inputs[0];
         UMat& buffer = internals[0];
-        startAxis = clamp(startAxis, inp0.dims);
-        endAxis = clamp(endAxis, inp0.dims);
+        startAxis = normalize_axis(startAxis, inp0.dims);
+        endAxis = normalize_axis(endAxis, inp0.dims);
 
         size_t num = total(shape(inp0.size), 0, startAxis);
         size_t numPlanes = total(shape(inp0.size), startAxis, endAxis + 1);
@@ -211,8 +211,8 @@ class NormalizeBBoxLayerImpl CV_FINAL : public NormalizeBBoxLayer
 
         const Mat& inp0 = inputs[0];
         Mat& buffer = internals[0];
-        startAxis = clamp(startAxis, inp0.dims);
-        endAxis = clamp(endAxis, inp0.dims);
+        startAxis = normalize_axis(startAxis, inp0.dims);
+        endAxis = normalize_axis(endAxis, inp0.dims);
 
         const float* inpData = inp0.ptr<float>();
         float* outData = outputs[0].ptr<float>();
@@ -378,8 +378,8 @@ class NormalizeBBoxLayerImpl CV_FINAL : public NormalizeBBoxLayer
 
         NormalizeConfiguration<float> config;
         config.input_shape.assign(std::begin(input_shape), std::end(input_shape));
-        config.axis_start = clamp(startAxis, input_shape.size());
-        config.axis_end = clamp(endAxis, input_shape.size()) + 1; /* +1 because NormalizeOp follows [start, end) convention */
+        config.axis_start = normalize_axis(startAxis, input_shape.size());
+        config.axis_end = normalize_axis(endAxis, input_shape.size()) + 1; /* +1 because NormalizeOp follows [start, end) convention */
         config.norm = pnorm;
         config.eps = epsilon;
 

modules/dnn/src/layers/reshape_layer.cpp

@@ -66,14 +66,7 @@ static void computeShapeByReshapeMask(const MatShape &srcShape,
     int srcShapeSize = (int)srcShape.size();
     int maskShapeSize = (int)maskShape.size();
 
-    if (srcRange == Range::all())
-        srcRange = Range(0, srcShapeSize);
-    else
-    {
-        int sz = srcRange.size();
-        srcRange.start = clamp(srcRange.start, srcShapeSize);
-        srcRange.end = srcRange.end == INT_MAX ? srcShapeSize : srcRange.start + sz;
-    }
+    srcRange = normalize_axis_range(srcRange, srcShapeSize);
 
     bool explicitMask = !maskShape.empty();  // All mask values are positive.
     for (int i = 0, n = maskShape.size(); i < n && explicitMask; ++i)

modules/dnn/src/layers/scale_layer.cpp

@@ -305,7 +305,7 @@ class ScaleLayerImpl CV_FINAL : public ScaleLayer
             numChannels = blobs[0].total();
 
         std::vector<size_t> shape(ieInpNode0->get_shape().size(), 1);
-        int cAxis = clamp(axis, shape.size());
+        int cAxis = normalize_axis(axis, shape.size());
         shape[cAxis] = numChannels;
 
         auto node = ieInpNode0;

modules/dnn/src/layers/slice_layer.cpp

@@ -153,7 +153,7 @@ class SliceLayerImpl : public SliceLayer
                 for (int j = 0; j < sliceRanges[i].size(); ++j)
                 {
                     if (shapesInitialized || inpShape[j] > 0)
-                        outputs[i][j] = clamp(sliceRanges[i][j], inpShape[j]).size();
+                        outputs[i][j] = normalize_axis_range(sliceRanges[i][j], inpShape[j]).size();
                 }
             }
         }
@@ -216,7 +216,7 @@ class SliceLayerImpl : public SliceLayer
             // Clamp.
             for (int j = 0; j < finalSliceRanges[i].size(); ++j)
             {
-                finalSliceRanges[i][j] = clamp(finalSliceRanges[i][j], inpShape[j]);
+                finalSliceRanges[i][j] = normalize_axis_range(finalSliceRanges[i][j], inpShape[j]);
             }
         }
 
@@ -634,7 +634,7 @@ class CropLayerImpl CV_FINAL : public SliceLayerImpl
         CV_Assert(inputs.size() == 2);
 
         MatShape dstShape = inputs[0];
-        int start = clamp(axis, dstShape);
+        int start = normalize_axis(axis, dstShape);
         for (int i = start; i < dstShape.size(); i++)
         {
             dstShape[i] = inputs[1][i];
@@ -653,7 +653,7 @@ class CropLayerImpl CV_FINAL : public SliceLayerImpl
         const Mat &inpSzBlob = inputs[1];
 
         int dims = inpBlob.dims;
-        int start_axis = clamp(axis, dims);
+        int start_axis = normalize_axis(axis, dims);
 
         std::vector<int> offset_final(dims, 0);
         if (offset.size() == 1)

modules/dnn/src/layers/softmax_layer.cpp

@@ -89,7 +89,7 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer
     {
         bool inplace = Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
         MatShape shape = inputs[0];
-        int cAxis = clamp(axisRaw, shape.size());
+        int cAxis = normalize_axis(axisRaw, shape.size());
         shape[cAxis] = 1;
         internals.assign(1, shape);
         return inplace;
@@ -124,7 +124,7 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer
 
         UMat& src = inputs[0];
         UMat& dstMat = outputs[0];
-        int axis = clamp(axisRaw, src.dims);
+        int axis = normalize_axis(axisRaw, src.dims);
 
         if (softmaxOp.empty())
         {
@@ -216,7 +216,7 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer
         const Mat &src = inputs[0];
         Mat &dst = outputs[0];
 
-        int axis = clamp(axisRaw, src.dims);
+        int axis = normalize_axis(axisRaw, src.dims);
         size_t outerSize = src.total(0, axis), channels = src.size[axis],
                 innerSize = src.total(axis + 1);
 
@@ -306,7 +306,7 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer
         auto context = reinterpret_cast<csl::CSLContext*>(context_);
 
         auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
-        auto channel_axis = clamp(axisRaw, input_wrapper->getRank());
+        auto channel_axis = normalize_axis(axisRaw, input_wrapper->getRank());
         return make_cuda_node<cuda4dnn::SoftmaxOp>(preferableTarget, std::move(context->cudnn_handle), channel_axis, logSoftMax);
     }
 #endif
@@ -315,7 +315,7 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer
     {
 #ifdef HAVE_VULKAN
         vkcom::Tensor in = VkComTensor(inputs[0]);
-        int cAxis = clamp(axisRaw, in.dimNum());
+        int cAxis = normalize_axis(axisRaw, in.dimNum());
         std::shared_ptr<vkcom::OpBase> op(new vkcom::OpSoftmax(cAxis, logSoftMax));
         return Ptr<BackendNode>(new VkComBackendNode(inputs, op));
 #endif  // HAVE_VULKAN
@@ -354,7 +354,7 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
 
         InferenceEngine::Builder::SoftMaxLayer ieLayer(name);
-        ieLayer.setAxis(clamp(axisRaw, input->getDims().size()));
+        ieLayer.setAxis(normalize_axis(axisRaw, input->getDims().size()));
 
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
@@ -365,7 +365,7 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
     {
         auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
-        int axis = clamp(axisRaw, ieInpNode->get_shape().size());
+        int axis = normalize_axis(axisRaw, ieInpNode->get_shape().size());
         auto softmax = std::make_shared<ngraph::op::v1::Softmax>(ieInpNode, axis);
         if (logSoftMax)
             return Ptr<BackendNode>(new InfEngineNgraphNode(std::make_shared<ngraph::op::v0::Log>(softmax)));